This paper presents a novel algorithm, Coherent and Uncorrelated Sources Estimation using Tensor Decomposition (cuse-td), designed to enhance the accuracy of Direction of Arrival (DOA) estimation in Multiple-Input Mul...
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This paper presents a novel algorithm, Coherent and Uncorrelated Sources Estimation using Tensor Decomposition (cuse-td), designed to enhance the accuracy of Direction of Arrival (DOA) estimation in Multiple-Input Multiple-Output (MIMO) systems. The cuse-td algorithm addresses the complex challenge of estimating both coherent and uncorrelated sources by leveraging advanced sensor-driven tensor decomposition techniques, with an emphasis on Tucker decomposition. By meticulously analyzing factor matrices derived from sensor data, the algorithm effectively decodes intricate spatiotemporal patterns within the received tensor, offering precise source estimation even in scenarios where the number and nature of the sources are unknown. The paper provides an in-depth assessment of the algorithm's performance, highlighting its computational efficiency and ability to handle diverse sensor-driven scenarios. Extensive simulations in intricate sensing environments demonstrate the algorithm's robustness, affirming its potential to significantly improve DOA estimation accuracy in MIMO systems across a wide range of operational conditions. The usefulness of the cuse-td algorithm lies in its applicability cuse -td to real-world MIMO systems, particularly in complex environments such as radar, wireless communications, and surveillance systems, where accurate source detection is critical for operational effectiveness. Its adaptability to varying sensor configurations and source conditions makes it a powerful tool for enhancing overall system reliability and performance.
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